Author and reference Application field Type of study Study category Study contribution Zhang et al. [137 ] Pattern recognition Developed Fuzzy and rough sets Presented a novel rough set model by integrating multigranulation rough sets over two universes and interval-valued hesitant fuzzy sets which is called interval-valued hesitant fuzzy multigranulation rough sets Bobillo and Straccia [138 ] Web ontology Proposed Fuzzy-rough sets Presented a solution related to fuzzy DLs and rough DLs which is called fuzzy-rough DL Liu [139 ] Axiomatic approaches Developed Fuzzy-rough sets Investigated the fixed universal set An et al. [140 ] Fuzzy-rough regression Developed Fuzzy-rough sets Analyzed the regression algorithm based on fuzzy partition, fuzzy-rough sets, estimation of regression values, and fuzzy approximation for estimating wind speed Riza et al. [141 ] Software packages Developed Fuzzy-rough sets Implementing and developing fuzzy-rough set theory and rough set theory algorithms in the Shiraz et al. [142 ] Fuzzy-rough DEA Proposed Fuzzy-rough sets Proposed a new fuzzy-rough DEA approach by combining the classical DEA, rough set, and fuzzy set theory to accommodate for uncertainty Saltos and Weber [11 ] Data mining Proposed Fuzzy-rough sets Introduced a new soft clustering model based on support vector clustering Zeng et al. [143 ] Big data Developed Fuzzy-rough sets Analyzed the changing mechanisms of the attribute values and fuzzy equivalence relations in fuzzy-rough sets Zhou et al. [144 ] Rough set-based clustering Developed Fuzzy-rough sets Developed a new approach for automatic selection of the threshold parameter for determining the approximation regions in rough set-based clustering Verbiest et al. [145 ] Prototype selection Proposed Fuzzy-rough sets Introduced the prototype selection model based on fuzzy-rough sets Vluymans et al. [19 ] Multi-instance learning Proposed Fuzzy-rough sets Introduced a novel kind of classifier for imbalanced multi-instance data based on fuzzy-rough set theory Pramanik et al. [146 ] Solid transportation Developed Fuzzy-rough sets Developed biobjective fuzzy-rough expected value approaches Liu [147 ] Binary relation Developed Fuzzy-rough sets Defined the concept of a solitary set for any binary relation from Meher [148 ] Pattern classification Developed Fuzzy-rough sets Developed the new rough fuzzy pattern classification approach by combining the merits of fuzzy and rough sets Kundu and Pal [149 ] Social networks Proposed Fuzzy-rough sets Proposed a new community detection algorithm to identify fuzzy-rough communities Ganivada et al. [150 ] Granular computing Proposed Fuzzy-rough sets Proposed the fuzzy-rough granular self-organizing map (FRGSOM), including the three-dimensional linguistic vector and connection weights for clustering patterns having overlapping regions Amiri and Jensen [151 ] Missing data imputation Proposed Fuzzy-rough sets Introduced three missing imputation approaches based on fuzzy-rough nearest neighbors, namely, VQNNI, OWANNI and FRNNI Ramentol et al. [152 ] Fuzzy-rough imbalanced learning Proposed Fuzzy-rough sets Introduced the use of data mining approaches in order to forecast the need of maintenance Affonso et al. [153 ] Artificial neural network Proposed Fuzzy-rough sets Proposed a new method for biological image classification by a rough-fuzzy artificial neural network Shukla and Kiridena [154 ] Dynamic supply chain Developed Fuzzy-rough sets Developed a new framework based on fuzzy-rough sets for configuring supply chain networks Pahlavani et al. [155 ] Remote Sensing Proposed Fuzzy-rough sets Proposed a novel fuzzy-rough set model to extract rules in the ANFIS based classification procedure for choosing the optimum features Xie and Hu [156 ] Fuzzy-rough set Proposed Fuzzy-rough sets Introduced a novel extended model based on three kinds of fuzzy-rough sets and two universes Zhao et al. [157 ] Constructing classifier Developed Fuzzy-rough sets Developed a rule-based classifier fuzzy-rough using one generalized fuzzy-rough model to introduce a novel idea which was called consistence degree Maji and Pal [158 ] Gene selection Proposed Fuzzy-rough sets Presented a new fuzzy equivalence partition matrix for approximating of the true marginal and joint distributions of continuous gene expression values Huang and Kuo [159 ] Cross-lingual Developed Fuzzy-rough sets Investigated two perspectives of cross-lingual semantic document similarity measures based on fuzzy sets and rough sets which was named formulation of similarity measures and document representation Wang et al. [160 ] Active learning Proposed Fuzzy-rough sets Proposed a new fuzzy-rough set approach for the sample’s inconsistency between decision labels and conditional features Ramentol et al. [161 ] Imbalanced classification Proposed Fuzzy-rough sets Developed a learning algorithm for considering the imbalance representation and proposed a classification algorithm for imbalanced data by using fuzzy-rough sets and ordered weighted average aggregation Zhang et al. [162 ] Parallel disassembly sequence planning Proposed Fuzzy-rough sets Proposed a new parallel disassembly sequence planning based on fuzzy-rough sets to reduce time complexity Derrac et al. [163 ] Prototype selection Proposed Fuzzy-rough sets Introduced a new fuzzy-rough set model for prototype selection based on optimizing the behavior of this classifier Verbiest et al. [164 ] Prototype selection Proposed Fuzzy-rough sets Improved The Synthetic Minority Over-Sampling Technique (SMOTE) to balance imbalanced data and proposed two prototype selection approaches based on fuzzy-rough sets Zhai [165 ] Fuzzy decision trees Proposed Fuzzy-rough sets Proposed new expanded attributes using significance of fuzzy conditional attributes with respect to fuzzy decision attributes Zhao et al. [166 ] Uncertainty measure Proposed Fuzzy-rough sets Introduced a novel complement information entropy method in fuzzy-rough sets based on arbitrary fuzzy relations, inner-class and outer-class information Hu et al. [167 ] Fuzzy-rough set Developed Fuzzy-rough sets Examined the properties of some existing fuzzy-rough sets in dealing with noisy data and proposed various robust approaches Changdar et al. [168 ] Genetic algorithm Proposed Fuzzy-rough sets Presented a new genetic-ant colony optimization algorithm in a fuzzy-rough set environment for solving problems related to the solid multiple Travelling Salesmen Problem (mTSP) Sun and Ma [169 ] Emergency plans evaluation Proposed Fuzzy-rough sets Introduced a novel model to evaluate emergency plans for unconventional emergency events using soft fuzzy-rough set theory
